Overview

Dataset statistics

Number of variables22
Number of observations10199
Missing cells144
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory371.3 B

Variable types

Numeric9
DateTime1
Categorical11
Boolean1

Alerts

technical_downtime_hrs has constant value "0.0"Constant
planned_maintenance_hrs has constant value "0.0"Constant
avg_delay_minutes has constant value "0.0"Constant
redundancy_flag has constant value "0"Constant
downtime_total has constant value "0.0"Constant
equipment_id is highly overall correlated with equipment_typeHigh correlation
equipment_type is highly overall correlated with equipment_idHigh correlation
idle_hrs is highly overall correlated with utilization_efficiency and 2 other fieldsHigh correlation
max_capacity_hrs is highly overall correlated with total_cases_day_lab and 1 other fieldsHigh correlation
total_cases_day_lab is highly overall correlated with max_capacity_hrsHigh correlation
utilization_efficiency is highly overall correlated with idle_hrs and 3 other fieldsHigh correlation
utilization_hrs is highly overall correlated with max_capacity_hrs and 3 other fieldsHigh correlation
utilization_pct is highly overall correlated with idle_hrs and 3 other fieldsHigh correlation
utilization_ratio is highly overall correlated with idle_hrs and 3 other fieldsHigh correlation
utilization_efficiency has 102 (1.0%) missing valuesMissing
record_id is uniformly distributedUniform
equipment_id is uniformly distributedUniform
record_id has unique valuesUnique
workflow_delay_events has 8028 (78.7%) zerosZeros
total_cases_day_lab has 117 (1.1%) zerosZeros

Reproduction

Analysis started2026-01-01 06:59:11.550788
Analysis finished2026-01-01 06:59:22.554398
Duration11 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

record_id
Real number (ℝ)

Uniform  Unique 

Distinct10199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5494.1192
Minimum319
Maximum10752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2026-01-01T12:29:22.650527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum319
5-th percentile829.9
Q12882.5
median5468
Q38099.5
95-th percentile10223.1
Maximum10752
Range10433
Interquartile range (IQR)5217

Descriptive statistics

Standard deviation3012.6171
Coefficient of variation (CV)0.54833486
Kurtosis-1.1993127
Mean5494.1192
Median Absolute Deviation (MAD)2609
Skewness0.017445768
Sum56034522
Variance9075861.9
MonotonicityStrictly increasing
2026-01-01T12:29:22.799529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107521
 
< 0.1%
3191
 
< 0.1%
3201
 
< 0.1%
3211
 
< 0.1%
3221
 
< 0.1%
3231
 
< 0.1%
3241
 
< 0.1%
3251
 
< 0.1%
3261
 
< 0.1%
3271
 
< 0.1%
Other values (10189)10189
99.9%
ValueCountFrequency (%)
3191
< 0.1%
3201
< 0.1%
3211
< 0.1%
3221
< 0.1%
3231
< 0.1%
3241
< 0.1%
3251
< 0.1%
3261
< 0.1%
3271
< 0.1%
3281
< 0.1%
ValueCountFrequency (%)
107521
< 0.1%
107511
< 0.1%
107501
< 0.1%
107491
< 0.1%
107481
< 0.1%
107471
< 0.1%
107461
< 0.1%
107451
< 0.1%
107441
< 0.1%
107431
< 0.1%

date
Date

Distinct731
Distinct (%)7.2%
Missing14
Missing (%)0.1%
Memory size79.8 KiB
Minimum2023-01-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-01T12:29:22.943004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:23.099522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

lab_id
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.8 KiB
Lab_CHN_01
5100 
Lab_CHN_02
5099 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters101990
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLab_CHN_02
2nd rowLab_CHN_01
3rd rowLab_CHN_01
4th rowLab_CHN_01
5th rowLab_CHN_01

Common Values

ValueCountFrequency (%)
Lab_CHN_015100
50.0%
Lab_CHN_025099
50.0%

Length

2026-01-01T12:29:23.239769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:23.330278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lab_chn_015100
50.0%
lab_chn_025099
50.0%

Most occurring characters

ValueCountFrequency (%)
_20398
20.0%
L10199
10.0%
a10199
10.0%
b10199
10.0%
C10199
10.0%
H10199
10.0%
N10199
10.0%
010199
10.0%
15100
 
5.0%
25099
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)101990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_20398
20.0%
L10199
10.0%
a10199
10.0%
b10199
10.0%
C10199
10.0%
H10199
10.0%
N10199
10.0%
010199
10.0%
15100
 
5.0%
25099
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)101990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_20398
20.0%
L10199
10.0%
a10199
10.0%
b10199
10.0%
C10199
10.0%
H10199
10.0%
N10199
10.0%
010199
10.0%
15100
 
5.0%
25099
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)101990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_20398
20.0%
L10199
10.0%
a10199
10.0%
b10199
10.0%
C10199
10.0%
H10199
10.0%
N10199
10.0%
010199
10.0%
15100
 
5.0%
25099
 
5.0%

equipment_id
Categorical

High correlation  Uniform 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size550.8 KiB
ICSI_02
1460 
INC_A1
1458 
INC_B1
1457 
MIC_X1
1457 
MIC_X2
1457 
Other values (2)
2910 

Length

Max length7
Median length6
Mean length6.2858123
Min length6

Characters and Unicode

Total characters64109
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowICSI_01
2nd rowINC_A1
3rd rowICSI_01
4th rowINC_A2
5th rowINC_B1

Common Values

ValueCountFrequency (%)
ICSI_021460
14.3%
INC_A11458
14.3%
INC_B11457
14.3%
MIC_X11457
14.3%
MIC_X21457
14.3%
INC_A21455
14.3%
ICSI_011455
14.3%

Length

2026-01-01T12:29:23.444892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:23.556702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
icsi_021460
14.3%
inc_a11458
14.3%
inc_b11457
14.3%
mic_x11457
14.3%
mic_x21457
14.3%
inc_a21455
14.3%
icsi_011455
14.3%

Most occurring characters

ValueCountFrequency (%)
I13114
20.5%
C10199
15.9%
_10199
15.9%
15827
9.1%
24372
 
6.8%
N4370
 
6.8%
S2915
 
4.5%
02915
 
4.5%
X2914
 
4.5%
M2914
 
4.5%
Other values (2)4370
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)64109
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I13114
20.5%
C10199
15.9%
_10199
15.9%
15827
9.1%
24372
 
6.8%
N4370
 
6.8%
S2915
 
4.5%
02915
 
4.5%
X2914
 
4.5%
M2914
 
4.5%
Other values (2)4370
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)64109
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I13114
20.5%
C10199
15.9%
_10199
15.9%
15827
9.1%
24372
 
6.8%
N4370
 
6.8%
S2915
 
4.5%
02915
 
4.5%
X2914
 
4.5%
M2914
 
4.5%
Other values (2)4370
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)64109
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I13114
20.5%
C10199
15.9%
_10199
15.9%
15827
9.1%
24372
 
6.8%
N4370
 
6.8%
S2915
 
4.5%
02915
 
4.5%
X2914
 
4.5%
M2914
 
4.5%
Other values (2)4370
 
6.8%

equipment_type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size600.6 KiB
ICSI_Workstation
2915 
Microscope
2914 
incubator
2254 
Incubator
2116 

Length

Max length16
Median length10
Mean length11.286401
Min length9

Characters and Unicode

Total characters115110
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowICSI_Workstation
2nd rowincubator
3rd rowICSI_Workstation
4th rowincubator
5th rowincubator

Common Values

ValueCountFrequency (%)
ICSI_Workstation2915
28.6%
Microscope2914
28.6%
incubator2254
22.1%
Incubator2116
20.7%

Length

2026-01-01T12:29:23.690388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:23.807101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
incubator4370
42.8%
icsi_workstation2915
28.6%
microscope2914
28.6%

Most occurring characters

ValueCountFrequency (%)
o16028
13.9%
t10200
 
8.9%
r10199
 
8.9%
c10198
 
8.9%
i8083
 
7.0%
I7946
 
6.9%
n7285
 
6.3%
a7285
 
6.3%
s5829
 
5.1%
u4370
 
3.8%
Other values (9)27687
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)115110
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o16028
13.9%
t10200
 
8.9%
r10199
 
8.9%
c10198
 
8.9%
i8083
 
7.0%
I7946
 
6.9%
n7285
 
6.3%
a7285
 
6.3%
s5829
 
5.1%
u4370
 
3.8%
Other values (9)27687
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)115110
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o16028
13.9%
t10200
 
8.9%
r10199
 
8.9%
c10198
 
8.9%
i8083
 
7.0%
I7946
 
6.9%
n7285
 
6.3%
a7285
 
6.3%
s5829
 
5.1%
u4370
 
3.8%
Other values (9)27687
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)115110
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o16028
13.9%
t10200
 
8.9%
r10199
 
8.9%
c10198
 
8.9%
i8083
 
7.0%
I7946
 
6.9%
n7285
 
6.3%
a7285
 
6.3%
s5829
 
5.1%
u4370
 
3.8%
Other values (9)27687
24.1%

max_capacity_hrs
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size528.0 KiB
18.0
7278 
10.0
2921 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters40796
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row18.0
3rd row18.0
4th row18.0
5th row18.0

Common Values

ValueCountFrequency (%)
18.07278
71.4%
10.02921
28.6%

Length

2026-01-01T12:29:23.944969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:24.041167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
18.07278
71.4%
10.02921
28.6%

Most occurring characters

ValueCountFrequency (%)
013120
32.2%
110199
25.0%
.10199
25.0%
87278
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)40796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013120
32.2%
110199
25.0%
.10199
25.0%
87278
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)40796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013120
32.2%
110199
25.0%
.10199
25.0%
87278
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)40796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013120
32.2%
110199
25.0%
.10199
25.0%
87278
17.8%

utilization_hrs
Real number (ℝ)

High correlation 

Distinct1533
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5651123
Minimum0
Maximum17.64
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2026-01-01T12:29:24.161189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.699
Q16.68
median9.39
Q312.49
95-th percentile15.861
Maximum17.64
Range17.64
Interquartile range (IQR)5.81

Descriptive statistics

Standard deviation3.7565414
Coefficient of variation (CV)0.39273364
Kurtosis-0.78612866
Mean9.5651123
Median Absolute Deviation (MAD)2.89
Skewness0.092246542
Sum97554.58
Variance14.111603
MonotonicityNot monotonic
2026-01-01T12:29:24.313541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.39115
 
1.1%
17.6485
 
0.8%
2.764
 
0.6%
4.560
 
0.6%
17.153
 
0.5%
16.245
 
0.4%
9.836
 
0.4%
9.536
 
0.4%
1.534
 
0.3%
926
 
0.3%
Other values (1523)9645
94.6%
ValueCountFrequency (%)
08
0.1%
0.031
 
< 0.1%
0.041
 
< 0.1%
0.051
 
< 0.1%
0.091
 
< 0.1%
0.311
 
< 0.1%
0.631
 
< 0.1%
0.671
 
< 0.1%
0.721
 
< 0.1%
0.781
 
< 0.1%
ValueCountFrequency (%)
17.6485
0.8%
17.621
 
< 0.1%
17.61
 
< 0.1%
17.591
 
< 0.1%
17.572
 
< 0.1%
17.561
 
< 0.1%
17.553
 
< 0.1%
17.531
 
< 0.1%
17.511
 
< 0.1%
17.51
 
< 0.1%

utilization_pct
Real number (ℝ)

High correlation 

Distinct90
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60874694
Minimum0.09
Maximum0.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2026-01-01T12:29:24.461519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.26
Q10.48
median0.62
Q30.74
95-th percentile0.9
Maximum0.98
Range0.89
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation0.18790371
Coefficient of variation (CV)0.30867295
Kurtosis-0.37855482
Mean0.60874694
Median Absolute Deviation (MAD)0.13
Skewness-0.29522665
Sum6208.61
Variance0.035307804
MonotonicityNot monotonic
2026-01-01T12:29:24.600736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62329
 
3.2%
0.64228
 
2.2%
0.7225
 
2.2%
0.6221
 
2.2%
0.66219
 
2.1%
0.58213
 
2.1%
0.72212
 
2.1%
0.67204
 
2.0%
0.68203
 
2.0%
0.71196
 
1.9%
Other values (80)7949
77.9%
ValueCountFrequency (%)
0.0923
 
0.2%
0.091
 
< 0.1%
0.12
 
< 0.1%
0.111
 
< 0.1%
0.134
 
< 0.1%
0.145
 
< 0.1%
0.15112
1.1%
0.1610
 
0.1%
0.1712
 
0.1%
0.1811
 
0.1%
ValueCountFrequency (%)
0.98127
1.2%
0.9720
 
0.2%
0.9619
 
0.2%
0.95107
1.0%
0.9461
0.6%
0.9331
 
0.3%
0.9256
0.5%
0.9160
0.6%
0.9112
1.1%
0.8973
0.7%

idle_hrs
Real number (ℝ)

High correlation 

Distinct1358
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0087734
Minimum0.2
Maximum14.915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2026-01-01T12:29:24.731416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.34
Q13.54
median5.59
Q38.09
95-th percentile12.14
Maximum14.915
Range14.715
Interquartile range (IQR)4.55

Descriptive statistics

Standard deviation3.2522785
Coefficient of variation (CV)0.54125497
Kurtosis-0.23314379
Mean6.0087734
Median Absolute Deviation (MAD)2.23
Skewness0.53802168
Sum61283.48
Variance10.577315
MonotonicityNot monotonic
2026-01-01T12:29:24.874738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.59113
 
1.1%
0.3691
 
0.9%
14.91584
 
0.8%
0.959
 
0.6%
13.558
 
0.6%
8.548
 
0.5%
1.848
 
0.5%
7.533
 
0.3%
0.232
 
0.3%
0.530
 
0.3%
Other values (1348)9603
94.2%
ValueCountFrequency (%)
0.232
 
0.3%
0.211
 
< 0.1%
0.242
 
< 0.1%
0.271
 
< 0.1%
0.281
 
< 0.1%
0.291
 
< 0.1%
0.32
 
< 0.1%
0.332
 
< 0.1%
0.3691
0.9%
0.383
 
< 0.1%
ValueCountFrequency (%)
14.91584
0.8%
14.861
 
< 0.1%
14.851
 
< 0.1%
14.791
 
< 0.1%
14.781
 
< 0.1%
14.772
 
< 0.1%
14.691
 
< 0.1%
14.681
 
< 0.1%
14.641
 
< 0.1%
14.632
 
< 0.1%

technical_downtime_hrs
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size518.0 KiB
0.0
10199 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30597
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010199
100.0%

Length

2026-01-01T12:29:25.008754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:25.124842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.010199
100.0%

Most occurring characters

ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

planned_maintenance_hrs
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size518.0 KiB
0.0
10199 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30597
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010199
100.0%

Length

2026-01-01T12:29:25.246508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:25.344182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.010199
100.0%

Most occurring characters

ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

workflow_delay_events
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24894598
Minimum0
Maximum5
Zeros8028
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2026-01-01T12:29:25.430851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51748465
Coefficient of variation (CV)2.0787026
Kurtosis5.2977666
Mean0.24894598
Median Absolute Deviation (MAD)0
Skewness2.1997388
Sum2539
Variance0.26779037
MonotonicityNot monotonic
2026-01-01T12:29:25.542372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
08028
78.7%
11843
 
18.1%
2291
 
2.9%
335
 
0.3%
41
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
08028
78.7%
11843
 
18.1%
2291
 
2.9%
335
 
0.3%
41
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
41
 
< 0.1%
335
 
0.3%
2291
 
2.9%
11843
 
18.1%
08028
78.7%

avg_delay_minutes
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size518.0 KiB
0.0
10199 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30597
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010199
100.0%

Length

2026-01-01T12:29:25.663352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:25.746092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.010199
100.0%

Most occurring characters

ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

primary_procedure
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size595.3 KiB
Fertilization_Check
2586 
ICSI
2566 
IVF
2534 
Embryo_Assessment
2513 

Length

Max length19
Median length17
Mean length10.758015
Min length3

Characters and Unicode

Total characters109721
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIVF
2nd rowEmbryo_Assessment
3rd rowIVF
4th rowICSI
5th rowEmbryo_Assessment

Common Values

ValueCountFrequency (%)
Fertilization_Check2586
25.4%
ICSI2566
25.2%
IVF2534
24.8%
Embryo_Assessment2513
24.6%

Length

2026-01-01T12:29:25.862111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:25.991719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fertilization_check2586
25.4%
icsi2566
25.2%
ivf2534
24.8%
embryo_assessment2513
24.6%

Most occurring characters

ValueCountFrequency (%)
e10198
 
9.3%
s10052
 
9.2%
i7758
 
7.1%
t7685
 
7.0%
I7666
 
7.0%
C5152
 
4.7%
F5120
 
4.7%
r5099
 
4.6%
_5099
 
4.6%
o5099
 
4.6%
Other values (14)40793
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)109721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e10198
 
9.3%
s10052
 
9.2%
i7758
 
7.1%
t7685
 
7.0%
I7666
 
7.0%
C5152
 
4.7%
F5120
 
4.7%
r5099
 
4.6%
_5099
 
4.6%
o5099
 
4.6%
Other values (14)40793
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)109721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e10198
 
9.3%
s10052
 
9.2%
i7758
 
7.1%
t7685
 
7.0%
I7666
 
7.0%
C5152
 
4.7%
F5120
 
4.7%
r5099
 
4.6%
_5099
 
4.6%
o5099
 
4.6%
Other values (14)40793
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)109721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e10198
 
9.3%
s10052
 
9.2%
i7758
 
7.1%
t7685
 
7.0%
I7666
 
7.0%
C5152
 
4.7%
F5120
 
4.7%
r5099
 
4.6%
_5099
 
4.6%
o5099
 
4.6%
Other values (14)40793
37.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
True
7283 
False
2916 
ValueCountFrequency (%)
True7283
71.4%
False2916
28.6%
2026-01-01T12:29:26.260054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

total_cases_day_lab
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4790666
Minimum-2
Maximum20
Zeros117
Zeros (%)1.1%
Negative61
Negative (%)0.6%
Memory size79.8 KiB
2026-01-01T12:29:26.368227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile2
Q15
median8
Q311
95-th percentile15
Maximum20
Range22
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1518689
Coefficient of variation (CV)0.48966108
Kurtosis-0.54496306
Mean8.4790666
Median Absolute Deviation (MAD)3
Skewness0.10683246
Sum86478
Variance17.238015
MonotonicityNot monotonic
2026-01-01T12:29:26.484149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
9869
 
8.5%
8865
 
8.5%
6855
 
8.4%
10838
 
8.2%
7837
 
8.2%
5756
 
7.4%
11749
 
7.3%
12693
 
6.8%
4627
 
6.1%
13581
 
5.7%
Other values (13)2529
24.8%
ValueCountFrequency (%)
-212
 
0.1%
-149
 
0.5%
0117
 
1.1%
1197
 
1.9%
2375
3.7%
3506
5.0%
4627
6.1%
5756
7.4%
6855
8.4%
7837
8.2%
ValueCountFrequency (%)
2016
 
0.2%
1935
 
0.3%
1873
 
0.7%
17135
 
1.3%
16229
 
2.2%
15332
3.3%
14453
4.4%
13581
5.7%
12693
6.8%
11749
7.3%

utilization_efficiency
Real number (ℝ)

High correlation  Missing 

Distinct1943
Distinct (%)19.2%
Missing102
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean60.857339
Minimum0
Maximum98
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2026-01-01T12:29:26.620522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26.488
Q148.2
median62.06
Q374.61
95-th percentile90
Maximum98
Range98
Interquartile range (IQR)26.41

Descriptive statistics

Standard deviation18.902313
Coefficient of variation (CV)0.31060039
Kurtosis-0.35925616
Mean60.857339
Median Absolute Deviation (MAD)13.16
Skewness-0.30463295
Sum614476.55
Variance357.29745
MonotonicityNot monotonic
2026-01-01T12:29:26.757468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98114
 
1.1%
1598
 
1.0%
9578
 
0.8%
2577
 
0.8%
9064
 
0.6%
56.524
 
0.2%
5822
 
0.2%
61.521
 
0.2%
7520
 
0.2%
6420
 
0.2%
Other values (1933)9559
93.7%
(Missing)102
 
1.0%
ValueCountFrequency (%)
08
0.1%
0.31
 
< 0.1%
0.41
 
< 0.1%
0.51
 
< 0.1%
0.91
 
< 0.1%
3.11
 
< 0.1%
3.51
 
< 0.1%
41
 
< 0.1%
5.831
 
< 0.1%
6.611
 
< 0.1%
ValueCountFrequency (%)
98114
1.1%
97.91
 
< 0.1%
97.891
 
< 0.1%
97.781
 
< 0.1%
97.721
 
< 0.1%
97.612
 
< 0.1%
97.61
 
< 0.1%
97.561
 
< 0.1%
97.53
 
< 0.1%
97.391
 
< 0.1%

year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing14
Missing (%)0.1%
Memory size547.9 KiB
2024.0
5097 
2023.0
5088 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters61110
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023.0
2nd row2023.0
3rd row2023.0
4th row2024.0
5th row2023.0

Common Values

ValueCountFrequency (%)
2024.05097
50.0%
2023.05088
49.9%
(Missing)14
 
0.1%

Length

2026-01-01T12:29:26.887038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:26.979066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2024.05097
50.0%
2023.05088
50.0%

Most occurring characters

ValueCountFrequency (%)
220370
33.3%
020370
33.3%
.10185
16.7%
45097
 
8.3%
35088
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)61110
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
220370
33.3%
020370
33.3%
.10185
16.7%
45097
 
8.3%
35088
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)61110
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
220370
33.3%
020370
33.3%
.10185
16.7%
45097
 
8.3%
35088
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)61110
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
220370
33.3%
020370
33.3%
.10185
16.7%
45097
 
8.3%
35088
 
8.3%

month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing14
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.5204713
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2026-01-01T12:29:27.079353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4499562
Coefficient of variation (CV)0.52909614
Kurtosis-1.209149
Mean6.5204713
Median Absolute Deviation (MAD)3
Skewness-0.0077998173
Sum66411
Variance11.902198
MonotonicityNot monotonic
2026-01-01T12:29:27.222074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3867
8.5%
1864
8.5%
12864
8.5%
8864
8.5%
7863
8.5%
5863
8.5%
10861
8.4%
11840
8.2%
6838
8.2%
4836
8.2%
Other values (2)1625
15.9%
ValueCountFrequency (%)
1864
8.5%
2791
7.8%
3867
8.5%
4836
8.2%
5863
8.5%
6838
8.2%
7863
8.5%
8864
8.5%
9834
8.2%
10861
8.4%
ValueCountFrequency (%)
12864
8.5%
11840
8.2%
10861
8.4%
9834
8.2%
8864
8.5%
7863
8.5%
6838
8.2%
5863
8.5%
4836
8.2%
3867
8.5%

redundancy_flag
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size498.1 KiB
0
10199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10199
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010199
100.0%

Length

2026-01-01T12:29:27.350043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:27.442296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
010199
100.0%

Most occurring characters

ValueCountFrequency (%)
010199
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)10199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
010199
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
010199
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
010199
100.0%

utilization_ratio
Real number (ℝ)

High correlation 

Distinct2003
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60852282
Minimum0
Maximum0.98
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2026-01-01T12:29:27.587420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.26595556
Q10.48333333
median0.61944444
Q30.746
95-th percentile0.90388889
Maximum0.98
Range0.98
Interquartile range (IQR)0.26266667

Descriptive statistics

Standard deviation0.188805
Coefficient of variation (CV)0.31026773
Kurtosis-0.35511684
Mean0.60852282
Median Absolute Deviation (MAD)0.13055556
Skewness-0.29208888
Sum6206.3242
Variance0.035647326
MonotonicityNot monotonic
2026-01-01T12:29:27.736921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.521666666791
 
0.9%
0.9885
 
0.8%
0.2577
 
0.8%
0.1564
 
0.6%
0.9553
 
0.5%
0.945
 
0.4%
0.1534
 
0.3%
0.9829
 
0.3%
0.9525
 
0.2%
0.93924
 
0.2%
Other values (1993)9672
94.8%
ValueCountFrequency (%)
08
0.1%
0.0031
 
< 0.1%
0.0041
 
< 0.1%
0.0051
 
< 0.1%
0.0091
 
< 0.1%
0.0311
 
< 0.1%
0.0351
 
< 0.1%
0.041
 
< 0.1%
0.058333333331
 
< 0.1%
0.066111111111
 
< 0.1%
ValueCountFrequency (%)
0.9829
 
0.3%
0.9885
0.8%
0.9791
 
< 0.1%
0.97888888891
 
< 0.1%
0.97777777781
 
< 0.1%
0.97722222221
 
< 0.1%
0.97611111112
 
< 0.1%
0.9761
 
< 0.1%
0.97555555561
 
< 0.1%
0.9753
 
< 0.1%

downtime_total
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size518.0 KiB
0.0
10199 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30597
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010199
100.0%

Length

2026-01-01T12:29:27.880069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T12:29:27.967828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.010199
100.0%

Most occurring characters

ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
020398
66.7%
.10199
33.3%

Interactions

2026-01-01T12:29:21.001749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:12.540555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:13.591383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:14.651286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:15.692371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:16.771641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:17.838110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:18.895946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:19.956036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:21.139776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:12.651956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:13.722622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:14.756501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:15.813878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:16.882174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:17.947391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:18.999761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:20.076451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:21.269164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:12.775757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:13.831887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:14.867761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:15.942708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:16.996726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:18.075919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:19.116962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:20.195466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:21.370324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:12.899549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:13.946216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:14.973133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:16.055421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:17.121527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:18.181632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:19.225238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:20.305284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:21.482651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:13.032231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:14.056633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:15.090501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:16.178584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:17.237689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:18.288571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:19.349197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:20.444861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:21.596385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:13.157558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:14.185246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:15.233193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:16.297868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:17.368220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:18.410131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:19.465610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:20.556900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:21.710535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:13.267849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:14.301631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:15.353121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:16.425012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:17.499880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:18.537056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:19.620497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:20.675363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:21.859337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:13.378356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:14.434277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:15.473706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:16.538134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:17.613678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:18.646808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:19.727835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:20.799787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:21.963568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:13.473958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:14.537290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:15.579276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:16.659410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:17.728548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:18.755904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:19.840921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T12:29:20.902456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-01T12:29:28.069180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
equipment_idequipment_typeidle_hrslab_idmax_capacity_hrsmonthprimary_procedurerecord_idredundancy_availabletotal_cases_day_labutilization_efficiencyutilization_hrsutilization_pctutilization_ratioworkflow_delay_eventsyear
equipment_id1.0000.8160.1560.0000.0000.0000.0140.0000.4000.0000.1790.1430.1790.1770.0810.000
equipment_type0.8161.0000.2200.0000.0080.0000.0140.0000.4000.0000.2510.2020.2520.2500.1050.000
idle_hrs0.1560.2201.0000.0060.440-0.0150.0100.0070.1380.291-0.854-0.394-0.850-0.850-0.0540.024
lab_id0.0000.0000.0061.0000.0000.0000.0000.0000.0000.0000.0240.0060.0200.0230.0220.000
max_capacity_hrs0.0000.0080.4400.0001.0000.0000.0000.0000.0000.7220.0300.6370.0150.0300.0160.000
month0.0000.000-0.0150.0000.0001.0000.0080.0100.000-0.0090.0110.0050.0130.013-0.0010.000
primary_procedure0.0140.0140.0100.0000.0000.0081.0000.0070.0030.0000.0130.0150.0130.0110.0130.000
record_id0.0000.0000.0070.0000.0000.0100.0071.0000.0220.011-0.007-0.002-0.006-0.0070.0010.029
redundancy_available0.4000.4000.1380.0000.0000.0000.0030.0221.0000.0000.1600.1320.1600.1590.0780.014
total_cases_day_lab0.0000.0000.2910.0000.722-0.0090.0000.0110.0001.0000.0070.4130.0070.002-0.0070.029
utilization_efficiency0.1790.251-0.8540.0240.0300.0110.013-0.0070.1600.0071.0000.7650.9951.0000.0560.000
utilization_hrs0.1430.202-0.3940.0060.6370.0050.015-0.0020.1320.4130.7651.0000.7540.7600.0390.000
utilization_pct0.1790.252-0.8500.0200.0150.0130.013-0.0060.1600.0070.9950.7541.0000.9860.0540.021
utilization_ratio0.1770.250-0.8500.0230.0300.0130.011-0.0070.1590.0021.0000.7600.9861.0000.0560.000
workflow_delay_events0.0810.105-0.0540.0220.016-0.0010.0130.0010.078-0.0070.0560.0390.0540.0561.0000.023
year0.0000.0000.0240.0000.0000.0000.0000.0290.0140.0290.0000.0000.0210.0000.0231.000

Missing values

2026-01-01T12:29:22.153495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-01T12:29:22.310724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-01T12:29:22.492610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

record_iddatelab_idequipment_idequipment_typemax_capacity_hrsutilization_hrsutilization_pctidle_hrstechnical_downtime_hrsplanned_maintenance_hrsworkflow_delay_eventsavg_delay_minutesprimary_procedureredundancy_availabletotal_cases_day_labutilization_efficiencyyearmonthredundancy_flagutilization_ratiodowntime_total
03192023-07-15Lab_CHN_02ICSI_01ICSI_Workstation10.08.160.821.840.00.000.0IVFTrue481.602023.07.000.8160000.0
13202023-07-27Lab_CHN_01INC_A1incubator18.015.600.872.400.00.000.0Embryo_AssessmentFalse1086.672023.07.000.8666670.0
23212023-10-24Lab_CHN_01ICSI_01ICSI_Workstation18.015.670.872.330.00.000.0IVFFalse287.062023.010.000.8705560.0
33222024-02-21Lab_CHN_01INC_A2incubator18.06.560.3610.040.00.010.0ICSITrue1536.442024.02.000.3644440.0
43232023-02-24Lab_CHN_01INC_B1incubator18.015.010.832.990.00.000.0Embryo_AssessmentTrue983.392023.02.000.8338890.0
53242024-05-10Lab_CHN_01MIC_X1Microscope18.07.490.4210.510.00.000.0Fertilization_CheckTrue1841.612024.05.000.4161110.0
63252024-08-31Lab_CHN_02ICSI_01ICSI_Workstation10.05.980.604.020.00.000.0IVFTrue359.802024.08.000.5980000.0
73262024-08-07Lab_CHN_02ICSI_02ICSI_Workstation18.011.970.666.030.00.000.0Fertilization_CheckTrue1166.502024.08.000.6650000.0
83272024-08-04Lab_CHN_02INC_B1incubator10.08.630.861.370.00.030.0IVFTrue386.302024.08.000.8630000.0
93282024-06-12Lab_CHN_02MIC_X2Microscope18.014.100.783.900.00.000.0IVFTrue1078.332024.06.000.7833330.0
record_iddatelab_idequipment_idequipment_typemax_capacity_hrsutilization_hrsutilization_pctidle_hrstechnical_downtime_hrsplanned_maintenance_hrsworkflow_delay_eventsavg_delay_minutesprimary_procedureredundancy_availabletotal_cases_day_labutilization_efficiencyyearmonthredundancy_flagutilization_ratiodowntime_total
10189107432024-04-29Lab_CHN_01ICSI_02ICSI_Workstation18.013.630.764.370.00.000.0IVFTrue1075.722024.04.000.7572220.0
10190107442024-03-22Lab_CHN_02ICSI_02ICSI_Workstation18.06.630.3711.370.00.010.0ICSITrue1136.832024.03.000.3683330.0
10191107452023-01-07Lab_CHN_02ICSI_01ICSI_Workstation10.07.040.702.960.00.000.0Fertilization_CheckFalse470.402023.01.000.7040000.0
10192107462024-02-23Lab_CHN_01ICSI_01ICSI_Workstation18.012.510.705.490.00.030.0Fertilization_CheckTrue1169.502024.02.000.6950000.0
10193107472024-06-11Lab_CHN_01MIC_X2Microscope18.011.760.654.120.00.000.0Embryo_AssessmentTrue665.332024.06.000.6533330.0
10194107482024-11-29Lab_CHN_02ICSI_02ICSI_Workstation18.014.870.833.130.00.000.0ICSITrue1182.612024.011.000.8261110.0
10195107492024-07-08Lab_CHN_02INC_A1Incubator18.013.120.734.880.00.000.0Fertilization_CheckTrue972.892024.07.000.7288890.0
10196107502024-01-08Lab_CHN_02MIC_X1Microscope18.011.600.646.400.00.010.0IVFTrue264.442024.01.000.6444440.0
10197107512023-04-07Lab_CHN_01INC_B1incubator18.011.690.656.310.00.000.0IVFTrue1064.942023.04.000.6494440.0
10198107522023-09-13Lab_CHN_02ICSI_01ICSI_Workstation18.08.710.489.290.00.000.0IVFTrue1148.392023.09.000.4838890.0